Diversity-Driven Widening of Hierarchical Agglomerative Clustering

In this paper we show that diversity-driven widening, the parallel exploration of the model space with focus on developing diverse models, can improve hierarchical agglomerative clustering. Depending on the selected linkage method, the model that is found through the widened search achieves a better...

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Bibliographic Details
Published inAdvances in Intelligent Data Analysis XIV Vol. 9385; pp. 84 - 94
Main Authors Fillbrunn, Alexander, Berthold, Michael R.
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2015
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Agglomerative Hierarchical Clustering Algorithm
Artificial intelligence
Davies-Bouldin Index (DBI)
Image processing
Initial Distance Matrix
Pattern recognition
Seeds Dataset
Silhouette Coefficient
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Summary:In this paper we show that diversity-driven widening, the parallel exploration of the model space with focus on developing diverse models, can improve hierarchical agglomerative clustering. Depending on the selected linkage method, the model that is found through the widened search achieves a better silhouette coefficient than its sequentially built counterpart.
ISBN:3319244647
9783319244648
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-24465-5_8